Fatigue Crack Growth Prognostics by Particle Filtering and Ensemble Neural Networks
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Abstract
Particle Filtering (PF) is a model-driven approach widely used in prognostics, which requires models of both the degradation process and the measurement acquisition system. In many practical cases, analytical models are not available, but a dataset containing a number of pairs component state - corresponding measurement may be available.
In this work, a data-driven approach based on a bagged ensemble of Artificial Neural Networks (ANNs) is adopted to build an empirical measurement model of a Particle Filter for the prediction of the Residual Useful Life (RUL) of a structure whose degradation process is described by a stochastic fatigue crack growth model of literature. The work focuses on the investigation of the capability of the
proposed approach to cope with the uncertainty affecting the RUL prediction.
How to Cite
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particle filtering, RUL, Ensemble of ANNs
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